PGS Educational Attainment
Import Data and create functions
Create functions
Cox Proportional Hazards: Model Assumption
# Function that fits Cox Proportional Harzards regression model
test_cox <- function(outcome_var_cut, predictor_var){
coxph(Surv(VISCODE, get(outcome_var_cut)) ~ get(predictor_var) + AGE + PTGENDER + AGE * PTGENDER, data = long_dat)
}
# Function that tests the model assumptions
test_cox_assump <- function(coxph_model){
# Testing Proportional Hazards
test.ph <- cox.zph(coxph_model)
print(test.ph)
print(ggcoxzph(test.ph))
# Testing Outliers & Influential Cases
print(ggcoxdiagnostics(coxph_model, type="dfbeta", linear.predictions = FALSE, ggtheme=theme_bw()))
}Plot survival analysis
Plots the survival analysis of VAR ~ thirtile_years and VAR ~ thirtile_PGS
plot_survival <- function(outcome_var_cut) {
# Create the Kaplan-Meier survival plot (MMSE~PGS)
par(mfrow = c(1, 2))
# Fit the Cox proportional hazards model (MMSE~PGS)
res.cox <- coxph(Surv(VISCODE, get(outcome_var_cut)) ~ thirtile_years + AGE + PTGENDER + AGE * PTGENDER, data = long_dat)
plot(survfit(res.cox),
main = paste("Survival Curve (", sub("_cut$", "", outcome_var_cut), "~years)"),
xlab = "Time",
ylab = "Probability",
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2,
xlim = c(0, 10),
cex.main = 0.95)
legend("bottomleft",
legend = c("Low Performance", "Average Performance", "High Performance"),
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2)
axis(1, lwd = 1) # Change x-axis thickness
axis(2, lwd = 1) # Change y-axis thickness)
# Fit the Cox proportional hazards model (MMSE~PGS)
res.cox <- coxph(Surv(VISCODE, get(outcome_var_cut)) ~ thirtile_PGS + AGE + PTGENDER + AGE * PTGENDER, data = long_dat)
plot(survfit(res.cox),
main = paste("Survival Curve (", sub("_cut$", "", outcome_var_cut), "~PGS)"),
xlab = "Time",
ylab = "Probability",
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2,
xlim = c(0, 10),
cex.main = 0.95)
legend("bottomleft",
legend = c("Low PGS", "Average PGS", "High PGS"),
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2)
axis(1, lwd = 1) # Change x-axis thickness
axis(2, lwd = 1) # Change y-axis thickness)
}Plots the surival curve of VAR ~ thirtile_years stratified by thirtile_PGS
plot_survival_strat <- function(outcome_var_cut) {
par(mfrow=c(1,3)) # Set parameters for plots
################################################################################
# MMSE~Thirtile Years for only low capacity individuals
################################################################################
# Fit the Kaplan-Meier estimator
surv_fit <- long_dat %>%
filter(thirtile_PGS == 1) %>%
coxph(Surv(VISCODE, get(outcome_var_cut)) ~ thirtile_years + AGE + PTGENDER + AGE * PTGENDER, data = .)
# Create the Kaplan-Meier survival plot
plot(survfit(surv_fit),
main = "Low EA capacity by Actual Years of Education",
xlab = "Time",
ylab = "Probability",
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2,
xlim = c(0, 10),
cex.main = 0.95)
legend("bottomleft",
legend = c("Low Performance", "Average Performance", "High Performance"),
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2)
axis(1, lwd = 1) # Change x-axis thickness
axis(2, lwd = 1) # Change y-axis thickness)
################################################################################
# MMSE~Thirtile Years for only average capacity individuals
################################################################################
# Fit the Kaplan-Meier estimator
surv_fit <- long_dat %>%
filter(thirtile_PGS == 2) %>%
coxph(Surv(VISCODE, get(outcome_var_cut)) ~ thirtile_years + AGE + PTGENDER + AGE * PTGENDER, data = .)
# Create the Kaplan-Meier survival plot
plot(survfit(surv_fit),
main = "Middle EA capacity by Actual Years of Education",
xlab = "Time",
ylab = "Probability",
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2,
xlim = c(0, 10),
cex.main = 0.95)
legend("bottomleft",
legend = c("Low Performance", "Average Performance", "High Performance"),
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2)
axis(1, lwd = 1) # Change x-axis thickness
axis(2, lwd = 1) # Change y-axis thickness)
################################################################################
# MMSE~Thirtile Years for only High capacity individuals
################################################################################
# Fit the Kaplan-Meier estimator
surv_fit <- long_dat %>%
filter(thirtile_PGS == 3) %>%
coxph(Surv(VISCODE, get(outcome_var_cut)) ~ thirtile_years + AGE + PTGENDER + AGE * PTGENDER, data = .)
# Create the Kaplan-Meier survival plot
plot(survfit(surv_fit),
main = "Low EA capacity by Actual Years of Education",
xlab = "Time",
ylab = "Probability",
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2,
xlim = c(0, 10),
cex.main = 0.95)
legend("bottomleft",
legend = c("Low Performance", "Average Performance", "High Performance"),
col = c("#021636", "#bbceed", "#0a77f5"),
lwd = 2)
axis(1, lwd = 1) # Change x-axis thickness
axis(2, lwd = 1) # Change y-axis thickness)
}Survival Analysis
Using the ntile function from dplyr, the lower tertile will be assigned value 1 (~ negative residual), middle tertile value 2 and upper tertile value 3 (~positive residual). The time-point is limited to the 9th follow-up (i.e., 48 months).
Mini-Mental State Examination (MMSE)
“The mini–mental state examination (MMSE) is a 30-point questionnaire that is used extensively in clinical and research settings to measure cognitive impairment. It is commonly used in medicine and allied health to screen for dementia. It is also used to estimate the severity and progression of cognitive impairment and to follow the course of cognitive changes in an individual over time; thus making it an effective way to document an individual’s response to treatment.Administration of the test takes between 5 and 10 minutes and examines functions including registration (repeating named prompts), attention and calculation, recall, language, ability to follow simple commands and orientation. […] Any score of 24 or more (out of 30) indicates a normal cognition. Below this, scores can indicate severe (≤9 points), moderate (10–18 points) or mild (19-23 points) cognitive impairment.” (Wikipedia.org). The MMSE scores were normalized using the NormPsy package and then the cut-off was calculated.
Boxplots of MMSE by Age Group at Baseline
To see if it is necessary to stratify for age groups effect of polygenic risk score for EA and age group was tested using linear regression. The results are displayed below.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
##
## Call:
## lm(formula = MMSE ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.234 -1.253 1.104 2.325 3.537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.44320 0.16858 162.795 < 2e-16 ***
## EA22 0.50376 0.11781 4.276 1.95e-05 ***
## Age_Group -0.11804 0.06882 -1.715 0.0864 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.382 on 3931 degrees of freedom
## (703 observations deleted due to missingness)
## Multiple R-squared: 0.005155, Adjusted R-squared: 0.004649
## F-statistic: 10.18 on 2 and 3931 DF, p-value: 3.878e-05##
## Call:
## lm(formula = MMSE_norm ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.524 -14.708 1.675 20.941 30.171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 76.5968 1.0058 76.159 < 2e-16 ***
## EA22 3.8988 0.7029 5.547 3.1e-08 ***
## Age_Group -0.6398 0.4106 -1.558 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.18 on 3931 degrees of freedom
## (703 observations deleted due to missingness)
## Multiple R-squared: 0.008129, Adjusted R-squared: 0.007625
## F-statistic: 16.11 on 2 and 3931 DF, p-value: 1.077e-07MMSE Survival Analysis
Next the survival analysis was conducted for the genetic capacity of educational attainment and the residual educational attainment.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.# Creates Cox Proportional Harzards regression model
res.cox <- test_cox("MMSE_cut", "thirtile_PGS")
# Tests model assumptions
test_cox_assump(res.cox)
## chisq df p
## get(predictor_var) 1.2776 1 0.26
## AGE 0.0154 1 0.90
## PTGENDER 1.6006 1 0.21
## AGE:PTGENDER 1.6788 1 0.20
## GLOBAL 3.0445 4 0.55## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the survminer package.
## Please report the issue at <https://github.com/kassambara/survminer/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'
# Testing Non-Linearity
ggcoxfunctional(Surv(VISCODE, MMSE_cut) ~ AGE + log(AGE) + sqrt(AGE), data = long_dat)## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
Alzheimer’s Disease Assessment Scale
The Cognitive Subscale Alzheimer’s Disease Assessment Scale (ADAS) is made of 11 tasks that include both subject-completed tests and observer-based assessments, assessing the memory, language, and praxis domains. The result is a global final score ranging from 0 to 70, based on the sum of the scores of the single tasks (ADAS11).
Beyond the ADAS11 score, the ADNI study included also an additional test of delayed word recall and a number cancellation or maze task, which are further summed to have a new total score that ranges from 0 to 85 (ADAS13).
In addition, the score of the task 4 (Word Recognition, ADASQ4) was included in the ADNIMERGE dataset.
ADAS11
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
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## chisq df p
## get(predictor_var) 1.2776 1 0.26
## AGE 0.0154 1 0.90
## PTGENDER 1.6006 1 0.21
## AGE:PTGENDER 1.6788 1 0.20
## GLOBAL 3.0445 4 0.55
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
ADAS13
“The ADAS13 was included as a global measure of cognitive function. ADAS13 is a test battery developed to assess severity of cognitive impairment associated with AD and includes subtests and clinical evaluations assessing memory function, reasoning, language function, orientation and praxis. The ADAS13 is a modified version of the original ADAS-Cog-11, adding a cancellation task and a delayed free recall task. The higher the scores, the more severe impairment of cognitive function.” (Mofrad et al., 2021)
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## chisq df p
## get(predictor_var) 0.1992 1 0.655
## AGE 0.0743 1 0.785
## PTGENDER 5.6686 1 0.017
## AGE:PTGENDER 5.7921 1 0.016
## GLOBAL 6.0466 4 0.196
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
ADASQ4
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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## useful. Consider adding a newdata argument.
CDRSB
“The clinical dementia rating (CDR) scale is commonly used to diagnose dementia due to Alzheimer’s disease (AD). The sum of boxes of the CDR (CDR-SB) has recently been emphasized and applied to interventional trials for tracing the progression of cognitive impairment (CI) in the early stages of AD.” (Tzeng et al., 2022)
See Table 3 for explanation on the staging category (O’Bryant et al., 2012)
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
DIGITSCORE
“The DSST (Digit Symbol Substitution Test) is a paper-and-pencil cognitive test presented on a single sheet of paper that requires a subject to match symbols to numbers according to a key located on the top of the page. The subject copies the symbol into spaces below a row of numbers. The number of correct symbols within the allowed time, usually 90 to 120 seconds, constitutes the score.” (Jaeger, 2018) The lower the scores, the more severe impairment of cognitive function.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
FAQ
The Functional Activities Questionnaire is used to assess an individual’s functional abilities in daily living activities. It is a caregiver-based questionnaire that helps evaluate how well a person is able to perform various instrumental activities of daily living (IADLs) and basic activities of daily living (ADLs). (ChatGPT) Sum scores (range 0-30). The score range for each item is 0–3 (higher scores indicate greater impairment; 0 = normal or never did but could do now; 1 = has difficulty but does by self or never did but would have difficulty now; 2 = requires assistance; 3 = dependent). There is no established cut-off score for IADL impairment on the FAQ. However, one study reported that a total FAQ score (sum of all 10 item scores; range 0–30) of ≥ 6 is suggestive of functional impairment [ 20]. (Marshall et al., 2015)
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
LDELTOTAL
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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MOCA
Reference literature: doi: 10.1111/j.1532-5415.2005.53221.x
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
Rey-Auditory Verbal Learning Test (RAVLT)
The RAVLT was included as a measure of memory function. In this test, the participants are asked to recall words from a list of 15 nouns immediately after each of five learning trials and after a short and a long delay. Two measures known to be sensitive to cognitive changes in patients with AD were included in the present study: Immediate recall (RAVLT-Im): the number of correct responses across the immediate recall of the five learning trials; percent forgetting (RAVLT-PF): the score on the fifth learning trial minus the score on the long delayed recall, divided by the score obtained on the fifth learning trial. The lower the scores, the more severe impairment of cognitive function.
Different summary scores are derived from raw RAVLT scores. These include RAVLT Immediate (the sum of scores from 5 first trials (Trials 1 to 5)), RAVLT Learning (the score of Trial 5 minus the score of Trial 1), RAVLT Forgetting (the score of Trial 5 minus score of the delayed recall) and RAVLT Percent Forgetting (RAVLT Forgetting divided by the score of Trial 5). We use naming of the ADNI merge table3 for these summary measures. We investigated the relationship between MRI measures and RAVLT cognitive test scores by estimating the RAVLT Immediate and RAVLT Percent Forgetting from the gray matter density. These two summary scores were selected since they highlight different aspects of episodic memory, learning (RAVLT Immediate) and delayed memory (RAVLT Percent forgetting), essential to AD and previous studies (Estévez-González et al., 2003, Wang et al., 2011, Gomar et al., 2014, Moradi et al., 2015) have indicated strong relationships between these two RAVLT measures and Alzheimer’s disease.
RAVLT Immediate
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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RAVLT Percentage Forgetting
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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RAVLT Forgetting
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
RAVLT Learning
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
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## Warning in survfit.coxph(surv_fit): the model contains interactions; the
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TRABSCORE
The Trail Making Test is a neuropsychological test of visual attention and task switching. It has two parts, in which the subject is instructed to connect a set of 25 dots as quickly as possible while maintaining accuracy.
The test can provide information about visual search speed, scanning, speed of processing, mental flexibility, and executive functioning. It is sensitive to cognitive impairment associated with dementia, including Alzheimer’s disease. (ChatGPT)
Record the total number of seconds to complete Part B (Trails B), up to a maximum of 300 seconds. If the participant is not finished by 300 seconds, the score is 300.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
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## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
Patient’s Everyday Cognition (EcogPt)
The original version of the ECog is an informant-based measure of cognitively-relevant everyday abilities comprised of 39 items, covering six cognitively-relevant domains: Everyday Memory, Everyday Language, Everyday Visuospatial Abilities, and Everyday Planning, Everyday Organization, and Everyday Divided Attention. Ratings are made on a four-point scale: 1 = better or no change compared to 10 years earlier, 2 = questionable/occasionally worse, 3 = consistently a little worse, 4 = consistently much worse. (Tomaszewski Farias et al., 2012)
EcogPt Everyday Divided Attention
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Everyday Language
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Everyday Memory
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Everyday Organization
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Everyday Planning
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Everyday Visuospatial Abilities
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogPt Total ???
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
Self-Reported Everyday Cognitive Abilities Questionnaire (EcogSP)
EcogSP Everyday Divided Attention
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Everyday Language
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Everyday Memory
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Everyday Organization
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Everyday Planning
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Everyday Visuospatial Abilities
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
EcogSP Total ???
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(res.cox): the model contains interactions; the default
## curve based on columm means of the X matrix is almost certainly not useful.
## Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
## Warning in survfit.coxph(surv_fit): the model contains interactions; the
## default curve based on columm means of the X matrix is almost certainly not
## useful. Consider adding a newdata argument.
Print session info:
## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survminer_0.4.9 ggpubr_0.6.0 ggplot2_3.4.2 rmdformats_1.0.4
## [5] gridExtra_2.3 NormPsy_1.0.8 survival_3.5-5 dplyr_1.1.2
##
## loaded via a namespace (and not attached):
## [1] mvtnorm_1.2-3 lattice_0.21-8 tidyr_1.3.0 zoo_1.8-12
## [5] rngWELL_0.10-9 digest_0.6.31 foreach_1.5.2 utf8_1.2.3
## [9] R6_2.5.1 backports_1.4.1 evaluate_0.21 highr_0.10
## [13] pillar_1.9.0 rlang_1.1.1 rstudioapi_0.14 data.table_1.14.8
## [17] car_3.1-2 jquerylib_0.1.4 Matrix_1.5-4.1 rmarkdown_2.21
## [21] labeling_0.4.2 splines_4.2.0 randtoolbox_2.0.4 munsell_0.5.0
## [25] lcmm_2.0.2 broom_1.0.4 compiler_4.2.0 xfun_0.39
## [29] pkgconfig_2.0.3 mgcv_1.8-42 htmltools_0.5.5 tidyselect_1.2.0
## [33] tibble_3.2.1 km.ci_0.5-6 bookdown_0.35 marqLevAlg_2.0.8
## [37] codetools_0.2-19 fansi_1.0.4 crayon_1.5.2 withr_2.5.0
## [41] grid_4.2.0 xtable_1.8-4 nlme_3.1-162 jsonlite_1.8.4
## [45] gtable_0.3.3 lifecycle_1.0.3 magrittr_2.0.3 KMsurv_0.1-5
## [49] scales_1.2.1 cli_3.6.1 cachem_1.0.8 carData_3.0-5
## [53] farver_2.1.1 ggsignif_0.6.4 doParallel_1.0.17 bslib_0.4.2
## [57] survMisc_0.5.6 generics_0.1.3 vctrs_0.6.2 iterators_1.0.14
## [61] tools_4.2.0 glue_1.6.2 purrr_1.0.1 abind_1.4-5
## [65] parallel_4.2.0 fastmap_1.1.1 yaml_2.3.7 colorspace_2.1-0
## [69] rstatix_0.7.2 knitr_1.43 sass_0.4.6